基于最优化算法配比寻优的油鸡饲料智能配方系统

吕阳; 高荣华; 赖成荣; 李奇峰; 耿爱莲; 王海宏; 丁露雨

doi:10.19998/j.cnki.2095-1795.2022.06.007

基于最优化算法配比寻优的油鸡饲料智能配方系统

doi: 10.19998/j.cnki.2095-1795.2022.06.007

吕阳^{1, 2,},
高荣华^{1, 2},
赖成荣^{1, 2},
李奇峰^{1, 2},
耿爱莲³,
王海宏³,
丁露雨^{1, 2,}

1.
北京市农林科学院信息技术研究中心，北京 100097
2.
国家农业信息化工程技术研究中心，北京 100097
3.
北京市农林科学院畜牧兽医研究所，北京 100097

基金项目: 北京市农林科学院创新能力建设项目（KJCX20200421）

详细信息

作者简介:
吕阳，学士，工程师，主要从事机器学习和深度学习研究　E-mail：lvy@nercita.org.cn

丁露雨，通信作者，博士，高级工程师，主要从事畜禽养殖过程信息化及智能化研究 E-mail：dingly@nercita.org.cn

中图分类号: S831.5
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- 被引次数: 0
出版历程
- 收稿日期: 2022-01-29
- 修回日期: 2022-04-15
- 出版日期: 2022-06-20

Intelligent Formula System of Oil Chicken Feed Based on Optimization Algorithm

LV Yang^{1, 2
,},
GAO Ronghua^{1, 2},
LAI Chengrong^{1, 2},
LI Qifeng^{1, 2},
GENG Ailian³,
WANG Haihong³,
DING Luyu^{1, 2
,}

1.
Research Center of Information Technology ，Beijing Academy of Agriculture and Forestry Sciences，Beijing 100097，China
2.
National Engineering Research Center for Information Technology in Agriculture，Beijing 100097，China
3.
Institute of Animal Husbandry and Veterinary Medicine，Beijing Academy of Agriculture and Forestry Sciences，Beijing 100097，China

摘要

摘要:
针对油鸡养殖饲料配方软件复杂不实用的问题，设计与开发了基于最优化算法配比寻优的油鸡饲料智能配方系统，实现饲料和营养标准的管理，同时具备饲料配方比例寻优功能。使用JAVA语言编写操作软件，运用Python编写内置寻优算法。系统具备原料管理、营养标准管理、历史饲料配方管理等功能，选择寻优效果较好的修正单纯形算法应用于本系统。油鸡饲料智能配方系统运行稳定，可满足用户需求。修正单纯形法配比寻优所需的平均时间为0.0310 s，准确率为100%；遗传算法计算配比寻优2 000次所需的平均时间为3.0200 s，准确率为0%。本系统成功实现了油鸡养殖过程中饲料配方相关的需求，并验证修正单纯形法在油鸡饲料配比寻优中的有效性，提高了配方计算效率。系统操作简单，便于中小养殖户使用，使先进的养殖技术融入基层养殖农户中，推动整个油鸡产业的健康可持续发展。
- 油鸡饲料 /
- 配方系统 /
- 修正单纯形算法 /
- 遗传算法
Abstract:
Aiming at complex and impractical problem of feed formula software for oil chicken breeding, an intelligent feed formula system based on an optimization algorithm was designed to meet feed and nutrition management standard and has function of feed formula proportion optimization．Operating software was in JAVA, and the built-in optimization algorithm was in Python．The system had raw material management function, nutrition management function and historical feed formula management function．A modified simplex method was used to solve problem of feed preparation proportion．Intelligent formula system of oil chicken feed run stably and could meet need of users．Average time required for proportion optimization of modified simplex method was 0.031 s, and accuracy was 100%．Average time required for genetic algorithm to calculate proportion optimization 2 000 times was 3.020 s, and accuracy was 0%．The system successfully realized requirements related to feeding formula in process of oil chicken breeding, verified effectiveness of optimization method of oil chicken feed ratio, and improved formula calculation efficiency．The system was easy to operate and convenient for small and medium-sized farms, could integrate advanced breeding technology into grass-roots farmers and promote healthy and sustainable development of whole oil chicken industry．
- fried chicken feed /
- formulation system /
- revised simplex method /
- genetic algorithm

HTML全文

图 1 油鸡饲料智能配方系统架构

Figure 1. Architecture diagram of intelligent formula system for oil chicken feed

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图 2 油鸡饲料智能配方系统功能框架

Figure 2. Functional framework of chicken feed intelligent formula system

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图 3 原料管理界面

Figure 3. Raw material management interface

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图 4 营养标准管理界面

Figure 4. Nutrition standard management interface

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图 5 历史饲料配方管理界面

Figure 5. Historical feed formula management interface

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图 6 配方比例寻优界面

Figure 6. Formula proportion optimization interface

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表 1 饲料营养成分占比

Table 1. Proportion of feed nutrients

属性名称	单位	属性名称	单位
鸡代谢能	MJ	钙	%
粗蛋白质	%	总磷	%
赖氨酸	%	有效磷	%
蛋氨酸+胱氨酸	%	单价	元/kg

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表 2 油鸡养殖营养推荐

Table 2. Recommended nutrition for oil chicken breeding

饲料用途	养殖阶段
种用	1 w～6 w
	7 w～18 w
	19 w～5%产蛋
	5%产蛋～43 w
	44 w以后
蛋用	1 w～6 w
	7 w～18 w
	19 w～5%开产
	5%开产～43 w
	43 w～淘汰
肉用	1 w～6 w
	7 w～11 w
	12 w～出栏

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$\begin{aligned} {\;}\\ {{\boldsymbol{x}}_{\boldsymbol{B}}}\end{aligned}\begin{array}{|c|c|}\hline {\boldsymbol{w}} & {{\boldsymbol{c}}_{\boldsymbol{B}}}{{\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\rightharpoonup}$}} {\boldsymbol b} }} \\\hline {{\boldsymbol{B}}^{{\boldsymbol{ - 1}}}} & {{\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\rightharpoonup}$}} {\boldsymbol b} }}\\\hline\end{array}$

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			x_k
	$\begin{aligned} {\;}\\ {{\boldsymbol{x}}_{\boldsymbol{B}}}\end{aligned}\begin{array}{\|c\|c\|}\hline {\boldsymbol{w}} & {{\boldsymbol{c}}_{\boldsymbol{B}}}{{\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\rightharpoonup}$}} {\boldsymbol b} }} \\\hline {{\boldsymbol{B}}^{{\boldsymbol{ - 1}}}} & {{\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\rightharpoonup}$}} {\boldsymbol b} }}\\\hline\end{array}$		$\begin{aligned} {\;}\\ \end{aligned}\begin{array}{\|c\|c\|}\hline \\[-8pt] {z}_k - c_k \\[1.5pt]\hline \\[-8pt] {\boldsymbol y}_k \\[1.5pt]\hline\end{array}$

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表 3 油鸡饲料智能配方系统测试示例

Table 3. Test example of intelligent formula system for oil chicken feed

名称	鸡代谢能/（ MJ·kg⁻¹）	粗蛋白/%	钙/%	总磷/%	有效磷/%	蛋氨酸+胱氨酸/%	赖氨酸/%	2021年价格/（元·kg⁻¹）	配比
玉米	13.60	8.50	0.16	0.25	0.05	0.33	0.36	2.13	0.60
小麦麸	5.69	15.70	0.11	0.92	0.32	0.55	0.63	1.42	0.15
大豆粕	10.00	44.20	0.33	0.62	0.16	1.24	2.68	2.77	0.15
豆油	35.02	0	0	0	0	0	0	8.00	0.05
石粉	0	0	38.00	0	0	0	0	0.35	0.05
营养标准	12.26	14.08	2.06	0.38	0.10	0.46	0.71	—	—
配方营养含量	12.26	14.08	2.06	0.38	0.10	0.46	0.71	2.32	1.00

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表 4 修正单纯形法和遗传算法测试结果

Table 4. Test results of modified simplex method and genetic algorithm

算法	准确率/%	平均计算时间/s
修正单纯形法	100	0.0310
遗传算法	0	3.0200

下载: 导出CSV

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